Differences in the frequencies of HLA-class I and II alleles between German patients with renal cell carcinoma and healthy controls

Integrative analysis reveals novel insights into juvenile idiopathic arthritis pathogenesis and shared molecular pathways with associated traits

Background

Juvenile idiopathic arthritis (JIA) is an autoimmune joint disease that frequently co-occurs with other complex phenotypes, including cancers and other autoimmune diseases. Despite the identification of numerous risk variants through genome-wide association studies (GWAS), the affected genes, their connection to JIA pathogenesis, and their role in the development of associated traits remain unclear. This study aims to address these gaps by elucidating the gene-regulatory mechanisms underlying JIA pathogenesis and exploring its potential role in the emergence of associated traits.

Methods

A two-sample Mendelian Randomization (MR) analysis was conducted to identify blood-expressed genes causally linked to JIA. A curated protein interaction network was subsequently used to identify sets of single-nucleotide polymorphisms (i.e., spatial eQTL SNPs) that regulate the expression of JIA causal genes and their protein interaction partners. These SNPs were cross-referenced against the GWAS catalog to identify statistically enriched traits associated with JIA.

Results

The two-sample MR analysis identified 52 genes whose expression changes in the blood are putatively causal for JIA. These genes (e.g., HLA, LTA, LTB, IL6ST) participate in a range of immune-related pathways (e.g., antigen presentation, cytokine signalling) and demonstrate cell type-specific regulatory patterns across different immune cell types (e.g., PPP1R11 in CD4+ T cells). The spatial eQTLs that regulate JIA causal genes and their interaction partners were statistically enriched for GWAS SNPs linked with 95 other traits, including both known and novel JIA-associated traits. This integrative analysis identified genes whose dysregulation may explain the links between JIA and associated traits, such as autoimmune/inflammatory diseases (genes at 6p22.1 locus), Hodgkin lymphoma (genes at 6p21.3 [FKBPL, PBX2, AGER]), and chronic lymphocytic leukemia (BAK1).

Conclusion

Our approach provides a significant advance in understanding the genetic architecture of JIA and associated traits. The results suggest that the burden of associated traits may differ among JIA patients, influenced by their combined genetic risk across different clusters of traits. Future experimental validation of the identified connections could pave the way for refined patient stratification, the discovery of new biomarkers, and shared therapeutic targets.

A promising Prognostic risk model for advanced renal cell carcinoma (RCC) with immune-related genes

Background

Renal cell carcinoma (RCC) is a third most common tumor of the urinary system. Nowadays, Immunotherapy is a hot topic in the treatment of solid tumors, especially for those tumors with pre-activated immune state.

Methods

In this study, we downloaded genomic and clinical data of RCC samples from The Cancer Genome Atlas (TCGA) database. Four immune-related genetic signatures were used to predict the prognosis of RCC by Cox regression analysis. Then we established a prognostic risk model consisting of the genes most related to prognosis from four signatures to value prognosis of the RCC samples via Kaplan–Meier (KM) survival analysis. An independent data from International Cancer Genome Consortium (ICGC) database were used to test the predictive stability of the model. Furthermore, we performed landscape analysis to assess the difference of gene mutant in the RCC samples from TCGA. Finally, we explored the correlation between the selected genes and the level of tumor immune infiltration via Tumor Immune Estimation Resource (TIMER) platform.

Results

We used four genetic signatures to construct prognostic risk models respectively and found that each of the models could divide the RCC samples into high- and low-risk groups with significantly different prognosis, especially in advanced RCC. A comprehensive prognostic risk model was constructed by 8 candidate genes from four signatures (HLA-B, HLA-A, HLA-DRA, IDO1, TAGAP, CIITA, PRF1 and CD8B) dividing the advanced RCC samples from TCGA database into high-risk and low-risk groups with a significant difference in cancer-specific survival (CSS). The stability of the model was verified by independent data from ICGC database. And the classification efficiency of the model was stable for the samples from different subgroups. Landscape analysis showed that mutation ratios of some genes were different between two risk groups. In addition, the expression levels of the selected genes were significantly correlated with the infiltration degree of immune cells in the advanced RCC.

Conclusions

Sum up, eight immune-related genes were screened in our study to construct prognostic risk model with great predictive value for the prognosis of advanced RCC, and the genes were associated with infiltrating immune cells in tumors which have potential to conduct personalized treatment for advanced RCC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-022-09755-2.

Relevance of HLA gene polymorphisms in Romanian patients with chronic renal insufficiency undergoing renal transplantation

BACKGROUND

Chronic renal insufficiency (CRI) is a global public health problem with a high incidence in the Romanian population. In this study, we aimed to investigate genomic HLA polymorphisms in Romanian patients with CRI waiting for kidney transplantation. To determine the existence of a potential strong link between certain HLA polymorphisms and CRI, we also looked at HLA specificity combinations within the same locus or even different loci, referring to randomly inherited allelic combinations rather than potential haplotypes.

METHODS

A total of 2199 patients with CRI on the kidney transplantation waiting list were included. A total of 2786 healthy individuals were included as controls. Both patients and controls were assessed for both HLA I and class II genes. HLA genes were typed using the low-resolution method polymerase chain reaction sequence-specific primer.

RESULTS

Certain class I and class II HLA allele groups, genotypes and haplotypes were significantly more frequent in patients with CRI than in the control individuals (eg B* 40 (p ≤ .001, pc ≤ .001), C* 12 (p ≤ .001, pc ≤ .001), DRB1*14 (p = .0022, pc = .04), C*12,- (p < .001, pc < .001), A*01-C*15 (p = .0003, pc = .03) and A*02-C*12 (p = .0005, pc = .0486)).

CONCLUSIONS

HLA gene polymorphisms could be clinically relevant CRI-associated genetic profiles in Romanian patients with CRI.

Comprehensive RNA-Sequencing Analysis in Peripheral Blood Cells Reveals Differential Expression Signatures with Biomarker Potential for Idiopathic Membranous Nephropathy

To date, the clinical course of idiopathic membranous nephropathy (iMN) remains unclear and lacks direct and effective diagnostic methods. To better understand the host gene expression changes involved in the iMN process and identify the potential signatures for clinical diagnosis, we performed a whole genome-wide transcriptome profile of peripheral blood cells (PBC) from patients with iMN and healthy controls (HCs). A total of 188 differentially expressed genes (DEGs) were detected in patients with iMN versus HCs. Gene ontology (GO) functional enrichment and Kyoto Encyclopedia of Genes and Genomes pathway analysis showed that these DEGs were mainly correlated with protein targeting, ion homeostasis GO terms, and ribosome and phagosome pathways. The top 10 differentially expressed protein-coding genes with >2-fold changes and high expression levels were validated using quantitative real-time PCR, and showed high consistency with the high-throughput sequencing results. HLA-C, S100A8, and FTH1 genes were selected for further validation and showed the most significant difference between the iMN and HC group, indicating that they could be used as potential clinical diagnostic biomarkers. Our results provide novel potential diagnostic signatures for iMN and have important implications for better understanding the pathogenesis of iMN.